UMBA 2 is simply a first pass at solving a fairly complex problem. Unfortunately, I don’t believe the rest of the solution can be solved only on a computer.
The research we’ve done so far has filled in one or two locations in our parameter space. That means that, of all the possible options for all the possible factors that we can control we’ve nailed down one or two. That is, we’ve used two pitches (Looper with seams on the top and a 2-seam 3:00 tilt pitch, both with 100% spin efficiency) and have extrapolated from them. I think what we have so far gives a qualitative idea about what will happen for a given pitch but, to really understand baseball aerodynamics and seam effects we need to fill in the parameter space. In other words, we need a lot more data.
The following list shows the parameters that, I believe, will cause changes in the behavior of the SSW.
- Spin rate
- Spin direction
- Effects of gyro
- Pitch speed
- Seam height
- Atmospheric effects (altitude, humidity, etc)
For each of these parameters, it would need to be shown (with PIV) how the separation map (see Fig. 1 below) changes as that parameter changes. It is likely that several of these parameters will affect one another and not in predictable ways. So, each parameter needs to be tested against each other parameter over the entire acceptable parameter space. That is, it’s going to be a lot of work.
As I see it, there are two potential methods for understanding this effect totally both have many difficulties. The two paths are 1) controlled lab experiments and PIV, 2) crowdsourcing pitches from human pitchers. There are pros and cons to each but, in theory, we should be able to arrive at the same point. From the perspective of modeling (mine at least), controlled lab experiments approach looks easier. Being given the fundamental behavior of the flow reduces the amount of guessing/trial and error I’d have to do. that being said, crowdsourcing is going to be cheaper.
The accuracy of the method depends on the ability to identify the pitched baseball’s initial conditions. That includes pitch speed and direction, pitch spin and direction, initial position, seam orientation, atmospheric conditions, and seam height. For human pitchers, it may be difficult, but not impossible, to accurately identify most of these parameters. There will be more errors when using multiple different human pitchers.
The controlled lab experiments allow us to control almost all of the parameters at will. With the pitching cannon, for example, it is far easier to change tilt by 15 minutes than it would be for a human pitcher to make a similar change. Additionally, all distances would be unchanging so, error from slightly different setups would not occur.
The seam height measurement is a major issue. Seam height plays a significant role in the SSW and flight path. In our most recent research, we found that a difference of 0.01 in. resulted in 20% more break from seams. Ideally, for each pitch, the seam height would be known. In our lab we’ve used a precision caliper to measure the diameter of the ball at multiple locations, both on and off the seams, to identify the seam height. It is a tedious and time-consuming task that requires several repetitions and is something that we typically give to the “new person.” When crowdsourcing the pitches the error from seam height variation would have to be accepted.